The field of out-of-distribution (OOD) detection is moving towards more effective and efficient methods for identifying unknown or unseen data. Recent developments have focused on improving the accuracy and robustness of OOD detection models, particularly in complex scenarios where rare object classes are often confused with truly unknown objects.

Noteworthy papers in this area have introduced innovative approaches, such as harnessing the power of diffusion models to synthesize auxiliary training data and uncertainty-aware likelihood ratio estimation methods. These advancements have led to significant improvements in OOD detection performance, including decreased false positive rates and increased average precision.

Some notable papers include: Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detection, which achieves the lowest average false positive rate among state-of-the-art methods. Boundary-based Out-Of-Distribution data generation (BOOD) surpasses the state-of-the-art method significantly, achieving a 29.64% decrease in average FPR95 and a 7.27% improvement in average AUROC on the CIFAR-100 dataset.